scholarly journals Quantum­Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Test Suite ◽  
Test Cases ◽  
Special Importance ◽  
Quantum Superposition ◽  
Complete Problem ◽  
Minimal Subset ◽  
Np Complete ◽  
Test Requirements

Test Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A brute­forceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantum­inspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.

scholarly journals Genetic Algorithm for Independent Job Scheduling in Grid Computing

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Muhanad Tahrir Younis ◽  
Shengxiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling ◽  
Computational Power ◽  
New Mutation ◽  
Complete Problem ◽  
Geographically Distributed ◽  
Np Complete ◽  
And Storage ◽  
Better Than

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature.

scholarly journals K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

2021 ◽  
Vol 27 (2) ◽  
pp. 170-189
Author(s):  
P. K. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage ◽  
Code Coverage ◽  
Test Case Selection ◽  
Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

A FAST ALGORITHM FOR BANDWIDTH MINIMIZATION

2007 ◽  
Vol 16 (03) ◽  
pp. 537-544 ◽  
Author(s):  
ANDREW LIM ◽  
BRIAN RODRIGUES ◽  
FEI XIAO
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Minimization Problem ◽  
Fast Algorithm ◽  
Hill Climbing ◽  
Solution Quality ◽  
Complete Problem ◽  
Np Complete ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

We propose a simple and direct node shifting method with hill climbing for the well-known matrix bandwidth minimization problem. Many heuristics have been developed for this NP-complete problem including the Cuthill-McKee (CM) and the Gibbs, Poole and Stockmeyer (GPS) algorithms. Recently, heuristics such as Simulated Annealing, Tabu Search and GRASP have been used, where Tabu Search and the GRASP with Path Relinking achieved significantly better solution quality than the CM and GPS algorithms. Experimentation shows that our method achieves the best solution quality when compared with these while being much faster than newly-developed algorithms.

scholarly journals On Minimum Representations of Matched Formulas

2014 ◽  
Vol 51 ◽  
pp. 707-723
Author(s):  
O. Cepek ◽  
S. Gursky ◽  
P. Kucera
Keyword(s):  
Normal Form ◽  
Boolean Function ◽  
Minimization Problem ◽  
Conjunctive Normal Form ◽  
Boolean Formula ◽  
Polynomial Hierarchy ◽  
Partial Assignment ◽  
Complete Problem ◽  
Boolean Minimization ◽  
Np Complete

A Boolean formula in conjunctive normal form (CNF) is called matched if the system of sets of variables which appear in individual clauses has a system of distinct representatives. Each matched CNF is trivially satisfiable (each clause can be satisfied by its representative variable). Another property which is easy to see, is that the class of matched CNFs is not closed under partial assignment of truth values to variables. This latter property leads to a fact (proved here) that given two matched CNFs it is co-NP complete to decide whether they are logically equivalent. The construction in this proof leads to another result: a much shorter and simpler proof of the fact that the Boolean minimization problem for matched CNFs is a complete problem for the second level of the polynomial hierarchy. The main result of this paper deals with the structure of clause minimum CNFs. We prove here that if a Boolean function f admits a representation by a matched CNF then every clause minimum CNF representation of f is matched.

scholarly journals Creating of model of genetic algorithm used to optimization of construction of jet engine element

Mechanik ◽  
2017 ◽  
Vol 90 (7) ◽  
pp. 603-605
Author(s):  
Adam Kozakiewicz ◽  
Rafał Kieszek
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Turbine Engines ◽  
Jet Engine ◽  
Complete Problem ◽  
Traditional Technology ◽  
Np Complete

In this paper authors show results of optimization of compressor discs in turbine engines. The problem of optimizing the thickness of the disc brought to the NP-complete problem, and solved it by using one of the genetic algorithms – evolutionary algorithm. Correctness of model and optimization algorithm were constantly checked. At the end of this paper, compressor disc created due to traditional technology and disc created by BLISK technology were compared.

scholarly journals Using Global Constraints to Automate Regression Testing

AI Magazine ◽  
2017 ◽  
Vol 38 (1) ◽  
pp. 73-87
Author(s):  
Arnaud Gotlieb ◽  
Dusica Marijan
Keyword(s):  
Product Line ◽  
Regression Testing ◽  
Test Suite ◽  
Global Constraints ◽  
Test Case ◽  
Delivery Mode ◽  
Test Cases ◽  
High Quality ◽  
Test Suite Reduction ◽  
Test Requirements

Nowadays, any communicating or autonomous systems rely on high-quality software-based components. To ensure a sufficient level of quality, these components must be thoroughly verified before being released and being deployed in operational settings. Regression testing is a crucial verification process that executes any new release of a software-based component against previous versions of the component, with existing test cases. However, the selection of test cases in regression testing is challenging as the time available for testing is limited and some selection criteria must be respected. This problem, coined as Test Suite Reduction (TSR), is usually addressed by validation engineers through manual analysis or by using approximation techniques. Even if the underlying optimization problem is untractable in theory, solving it in practice is crucial when there are pressing needs to release high-quality components while at the same time reducing the time-to-market of new software releases. In this paper, we address the TSR problem with sound Artificial intelligence techniques such as Constraint Programming (CP) and global constraints. By associating each test case a cost-value aggregating distinct criteria, such as execution time, priority or importance due to the error-proneness of each test case, we propose several constraint optimization models to find a subset of test cases covering all the test requirements and optimizing the overall cost of selected test cases. Our models are based on a combination of NVALUE, GLOBALCARDINALITY, and SCALAR_PRODUCT, three well-known global constraints that can faithfully encode the coverage relation between test cases and test requirements. Our contribution includes the reuse of existing preprocessing rules to simplify the problem before solving it and the design of structure-aware heuristics, which take into account the notion of costs, associated with test cases. The work presented in this paper has been motivated by an industrial application in the communication domain. Our overall goal is to develop a constraint-based approach of test suite reduction that can be deployed to test a complete product line of conferencing systems in continuous delivery mode. By implementing this approach in a software prototype tool and experimentally evaluated it on both randomly generated instances and industrial instances, we hope to foster a quick adoption of the technology.

scholarly journals On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1145 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri ◽  
Rinkaj Goyal
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Execution ◽  
Mutation Strategy

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

scholarly journals Image Segmentation using Improved Genetic Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1784-1792
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Approximate Solutions ◽  
Threshold Value ◽  
Complex Task ◽  
Metaheuristic Algorithm ◽  
Improved Genetic Algorithm ◽  
Complete Problem ◽  
Np Complete ◽  
Multi Level

Segmentation of image is a complex task. To recognize an image, segmentation is essential parts. During image segmentation, subsets of images on the basis of some features like gray levels values of pixels or position of pixels find out. This is an NP-complete problem, to find the solution to such problems is a computationally hard task. Some heuristic algorithm can be used to find out the solution to such a hard task. These algorithms find approximate solutions. Exact solution of such problems is not possible. Genetic algorithm can be considered a metaheuristic algorithm used the evolution of the population of solutions. This paper proposedan improved Genetic Algorithm that used to find multi-level thresholding segmentation of the image. The threshold value can be calculated by cumulative histogram and satisfactory result have been given by the experiments done on test images that are taken from Mnist datasets

Sign in / Sign up

Export Citation Format

Share Document